In order to form a decentralized FL system based on blockchain technology, we use the consortium setting [1]} that leverages blockchain to realize the decentralized FL transaction, transparently evaluate the participants' contributions to the global model, and develop a decentralized incentive system. In this experiment, MNIST [2]} datasets are used as a standard image classification with 10.000 images for the testing examples and 60.000 images for the training examples. Each example is a 28 × 28 size gray-level image. Figure REF  shows the initial migration and deploying smart contracts using the Ethereum platform. The figure shows that we need 164391 (0.00328782 ETH), 263330 (0.0052666 ETH), and 1018839 (0.02037678 ETH) units of gas for initial migration, federated smart contract, and deploying participants' contribution implementation, respectively. After deploying smart contracts, we can customize the number of participants involved for local model training and calculate their contribution fairly based on blockchain. Then, to implement the FL with DP model, we use a python-based open-source library that IBM developed for the experimentation, simulation, and deployment of differential privacy tools and applications [3]}. Figure REF  shows DP-based FL experiment using different privacy budget degrees, i.e., \(\epsilon =4.03\) , \(\epsilon =1.18\)  and \(\epsilon =0.522\) , in 15 epochs. According to simulation results, Figure REF  with privacy budget \(\epsilon =4.03\)  achieves 95.80000024915344 model accuracy. In contrast, Figure REF  and REF  with privacy budget \(\epsilon =1.18\)  and \(\epsilon =0.522\) , generates a model accuracy of 93.77999901771545 and 89.31000232696533, respectively. Thus, the smaller value of \(\epsilon \)  (i.e., more noise added) generates higher privacy (i.e., based on the gap between accuracy and validation) but lower accuracy, and vice versa.
